Objective: Soft tissue deformation and ruptures complicate needle placement. However, ruptures at tissue interfaces also contain information which helps physicians to navigate through different layers. This navigation task can be challenging, whenever ultrasound (US) image guidance is hard to align and externally sensed forces are superimposed by friction. Methods: We propose an experimental setup for reproducible needle insertions, applying optical coherence tomography (OCT) directly at the needle tip as well as external US and force measurements. Processing the complex OCT data is challenging as the penetration depth is limited and the data can be difficult to interpret. Using a machine learning approach, we show that ruptures can be detected in the complex OCT data without additional external guidance or measurements after training with multi-modal ground-truth from US and force. Results: We can detect ruptures with accuracies of 0.94 and 0.91 on homogeneous and inhomogeneous phantoms, respectively, and 0.71 for ex-situ tissues. Conclusion: We propose an experimental setup and deep learning based rupture detection for the complex OCT data in front of the needle tip, even in deeper tissue structures without the need for US or force sensor guiding. Significance: This study promises a suitable approach to complement a robust robotic needle placement.

The temporal resolution of the tomographic imaging method magnetic particle imaging (MPI) is remarkably high. The spatial resolution is degraded for measured voltage signal with low signal-to-noise ratio, because the regularization in the image reconstruction step needs to be increased for system-matrix approaches and for deconvolution steps in x-space approaches. To improve the signal-to-noise ratio, blockwise averaging of the signal over time can be advantageous. However, since block-wise averaging decreases the temporal resolution, it prevents resolving the motion. In this paper, a framework for averaging motion-corrupted MPI raw data is proposed. The motion is considered to be periodic as it is the case for respiration and/or the heartbeat. The same state of motion is thus reached repeatedly in a time series exceeding the repetition time of the motion and can be used for averaging. As the motion process and the acquisition process are, in general, not synchronized, averaging of the captured MPI raw data corresponding to the same state of motion requires to shift the starting point of the individual frames. For high-frequency motion, a higher frame rate is potentially required. To address this issue, a binning method for using only parts of complete frames from a motion cycle is proposed that further reduces the motion artifacts in the final images. The frequency of motion is derived directly from the MPI raw data signal without the need to capture an additional navigator signal. Using a motion phantom, it is shown that the proposed method is capable of averaging experimental data with reduced motion artifacts. The methods are further validated on in-vivo data from mouse experiments to compensate the heartbeat.

Purpose: Elasticity of soft tissue provides valuable information to physicians during treatment and diagnosis of diseases. A number of approaches have been proposed to estimate tissue stiffness from the shear wave velocity. Optical coherence elastography offers a particularly high spatial and temporal resolution. However, current approaches typically acquire data at different positions sequentially, making it slow and less practical for clinical application. Methods: We propose a new approach for elastography estimations using a fast imaging device to acquire small image volumes at rates of 831 Hz. The resulting sequence of phase image volumes is fed into a 4D convolutional neural network which handles both spatial and temporal data processing. We evaluate the approach on a set of image data acquired for gelatin phantoms of known elasticity. Results: Using the neural network, the gelatin concentration of unseen samples was predicted with a mean error of 0.65 ± 0.81 percentage points from 90 subsequent volumes of phase data only. We achieve a data acquisition and data processing time of under 12 ms and 22 ms, respectively. Conclusions: We demonstrate direct volumetric optical coherence elastography from phase image data. The approach does not rely on particular stimulation or sampling sequences and allows the estimation of elastic tissue properties of up to 40 Hz.

An important aspect of robotic radiation therapy is active compensation of target motion. Recently, ultrasound has been proposed to obtain real-time volumetric images of abdominal organ motion. One approach to realize flexible probe placement throughout the treatment fraction is based on a robotic arm holding the ultrasound probe. However, the probe and the robot holding it may obstruct some of the beams with a potentially adverse effect on the plan quality. This can be mitigated by using a kinematically redundant robot, which allows maintaining a steady pose of the ultrasound probe while moving its elbow in order to minimize beam blocking. Ultimately, the motion of both the beam source carrying and the ultrasound probe holding robot contributes to the overall treatment time, i.e. beam delivery and robot motion. We propose an approach to optimize the motion and coordination of both robots based on a generalized traveling salesman problem. Furthermore, we study an application of the model to a prostate treatment scenario. Because the underlying optimization problem is hard, we compare results from a state-of-the-art heuristic solver and an approximation scheme with low computational effort. Our results show that integration of the robot holding the ultrasound probe is feasible with acceptable overhead in overall treatment time. For clinically realistic velocities of the robots, the overhead is less than 4% which is a small cost for the added benefit of continuous, volumetric, and non-ionizing tracking of organ motion over periodic x-ray-based tracking.